Poor Efficacy of Immune Checkpoint Inhibitors Plus Chemotherapy in Lung Cancer Patients with EGFR/ERBB2 Exon 20 Insertion
Bibliographic record
Abstract
Background: EGFR and ERBB2 exon 20 insertion (Ex20ins) account for a small fraction of patients with EGFR mutations. The efficacy of immune checkpoint inhibitors (ICIs) for these patients was still controversial. Methods: This retrospective study enrolled lung cancer patients harboring either EGFR or ERBB2 Ex20ins mutations. All the patients were treated with platinum-based chemotherapy plus ICIs, or platinum-based chemotherapy. The demographic features and clinical outcome of each patient were reviewed and analyzed. Results: When treated with immunochemotherapy, patients with EGFR/ERBB2 Ex20ins mutations (n = 31) had poor PFS compared with those without EGFR mutations (n = 141, 5.0 mon and 11.2 mon, p < 0.001). When compared with those with EGFR classic mutations who received immunotherapy as the salvage therapy (n = 24), these patients with EGFR/ERBB2 Ex20ins mutations had similar PFS (5.0 mon and 4.1 mon, p = 0.625), ORR (37.5% vs. 48.4%), and DCR (70.8% vs. 77.4%). In the patients with EGFR/ERBB2 Ex20ins mutations, the PFS of those treated with chemotherapy (n = 54) and those treated with immunochemotherapy (n = 31) was 6.5 mon vs. 5.0 mon (p = 0.066). In the EGFR Ex20ins subgroup, the PFS of addition of bevacizumab to chemotherapy (n = 20) and chemotherapy alone (n = 16) was 8.8 mon and 5.2 mon, respectively (p = 0.082) or immunochemotherapy (n = 15, 8.8 mon and 5.0 mon, p = 0.097). Similarly, in the ERBB2 subgroup, the combination of bevacizumab and chemotherapy achieved a numerically longer PFS over chemotherapy alone (9.1 mon and 4.5 mon, p = 0.253), but there was no statistical significance. Conclusions: This study showed that platinum-based chemotherapy plus ICIs had limited efficiency compared to platinum-based chemotherapy for patients with EGFR/ERBB2 Ex20ins. Chemotherapy plus bevacizumab may be a potential scheme for these patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".